论文标题
使用触觉 - 视觉传感器对未知物体的质量强大掌握计划
Center-of-Mass-based Robust Grasp Planning for Unknown Objects Using Tactile-Visual Sensors
论文作者
论文摘要
不稳定的抓握姿势可以导致滑动，因此可以通过滑动检测来预测不稳定的抓握姿势。之后，需要重新制定重新纠正抓紧姿势才能完成任务。在这项工作中，我们提出了一个具有多传感器模块的新型重新制定者，以通过滑动探测器的反馈来计划掌握调整。然后，训练了一个重新制定者，以估计质量中心的位置，这有助于机器人找到最佳的抓地力。这项工作中的数据集由一对触觉传感器，一个RGB-D摄像头和一个配备了联合力量/扭矩传感器的Franka Emika机器人臂收集的1 025个滑移实验和1 347个重新制定。我们表明，与基于最先进的基于最先进的视觉掌握算法相比，我们的算法可以成功地检测和分类5个未知测试对象的滑移，并将其精度为76.88％，而Regrasp Planner则将掌握率提高了31.0％。
An unstable grasp pose can lead to slip, thus an unstable grasp pose can be predicted by slip detection. A regrasp is required afterwards to correct the grasp pose in order to finish the task. In this work, we propose a novel regrasp planner with multi-sensor modules to plan grasp adjustments with the feedback from a slip detector. Then a regrasp planner is trained to estimate the location of center of mass, which helps robots find an optimal grasp pose. The dataset in this work consists of 1 025 slip experiments and 1 347 regrasps collected by one pair of tactile sensors, an RGB-D camera and one Franka Emika robot arm equipped with joint force/torque sensors. We show that our algorithm can successfully detect and classify the slip for 5 unknown test objects with an accuracy of 76.88% and a regrasp planner increases the grasp success rate by 31.0% compared to the state-of-the-art vision-based grasping algorithm.
